This paper describes the concept of sensor networks which has been made viable by the convergence of microelectro-mechanical systems technology, wireless communications and digital electronics. First, the sensing tasks and the potential sensor networks applications are explored, and a review of factors influencing the design of sensor networks is provided. Then, the communication architecture for sensor networks is outlined, and the algorithms and protocols developed for each layer in the literature are explored. Open research issues for the realization of sensor networks are

Abstract — This paper presents and analyzes an architecture that exploits the serendipitous movement of mobile agents in an environment to collect sensor data in sparse sensor networks. The mobile entities, called MULEs, pick up data from sensors when in close range, buffer it, and drop off the data to wired access points when in proximity. This leads to substantial power savings at the sensors as they only have to transmit over a short range. Detailed performance analysis is presented based on a simple model of the system incorporating key system variables such as number of MULEs, sensors and access points. The performance metrics observed are the data success rate (the fraction of generated data that reaches the access points) and the required buffer capacities on the sensors and the MULEs. The modeling along with simulation results can be used for further analysis and provide certain guidelines for deployment of such systems. I.

Recent advances in wireless sensor networks have led to many new protocols specifically designed for sensor networks where energy awareness is an essential consideration. Most of the attention, however, has been given to the routing protocols since they might differ depending on the application and network architecture. This paper surveys recent routing protocols for sensor networks and presents a classification for the various approaches pursued. The three main categories explored in this paper are data-centric, hierarchical and location-based. Each routing protocol is described and discussed under the appropriate category. Moreover, protocols using contemporary methodologies such as network flow and QoS modeling are also discussed. The paper concludes with open research issues. 1.

Evolving networks of ad-hoc, wireless sensing nodes rely heavily on the ability to establish position information. The algorithms presented herein rely on range measurements between pairs of nodes and the aprioricoordinates of sparsely located anchor nodes. Clusters of nodes surrounding anchor nodes cooperatively establish confident position estimates through assumptions, checks, and iterative refinements. Once established, these positions are propagated to more distant nodes, allowing the entire network to create an accurate map of itself. Major obstacles include overcoming inaccuracies in range measurements as great as ±50%, as well as the development of initial guesses for node locations in clusters with few or no anchor nodes. Solutions to these problems are presented and discussed, using position error as the primary metric. Algorithms are compared according to position error, scalability, and communication and computational requirements. Early simulations yield average position errors of 5 % in the presence of both range and initial position inaccuracies. 1.

This article describes architectural and algorithmic approaches that designers can use to enhance the energy awareness of wireless sensor networks. The article starts off with an analysis of the power consumption characteristics of typical sensor node architectures and identifies the various factors that affect system lifetime. We then present a suite of techniques that perform aggressive energy optimization while targeting all stages of sensor network design, from individual nodes to the entire network. Maximizing network lifetime requires the use of a well-structured design methodology, which enables energy -aware design and operation of all aspects of the sensor network, from the underlying hardware platform to the application software and network protocols. Adopting such a holistic approach ensures that energy awareness is incorporated not only into individual sensor nodes but also into groups of communicating nodes and the entire sensor network. By following an energy-aware design methodology based on techniques such as in this article, designers can enhance network lifetime by orders of magnitude.

Self-config uration in wireless sensor networks is ag eneral class of estimation problems which we study via the Cramer-Rao bound (CRB).Specifically, we consider sensor location estimation when sensors measure received sig]P strengI (RSS) or time-of-arrival (TOA) between themselves and neig boring sensors.A small fraction of sensors in the network have known location while the remaining locations must be estimated.We derive CRBs and maximum-likelihood estimators (MLEs) under Gaussian and log -normal models for the TOA and RSS measurements, respectively.An extensive TOA and RSS measurement campaig in an indoor o#ce area illustrates MLE performance.Finally, relative location estimation alg orithms are implemented in a wireless sensor network testbed and deployed in indoor and outdoor environments.The measurements and testbed experiments demonstrate 1 m RMS location errorsusing TOA, and 1 m to 2 m RMS location errors using RSS. Index Terms sensor position location estimation, radio channel measurement, sig nal streng h, time-ofarrival, wireless sensor network testbed, self-config uration, Cramer-Rao bound I.

A key challenge in ad-hoc, data-gathering wireless sensor networks is achieving a lifetime of several years using nodes that carry merely hundreds of joules of stored energy. In this paper, we explore the fundamental limits of energy-efficient collaborative data-gathering by deriving upper bounds on the lifetime of increasingly sophisticated sensor networks.

In all-wireless networks a crucial problem is to minimize energy consumption, as in most cases the nodes are batteryoperated. We focus on the problem of power-optimal broadcast, for which it is well known that the broadcast nature of the radio transmission can be exploited to optimize energy consumption. Several authors have conjectured that the problem of power-optimal broadcast is NP-complete. We provide here a formal proof, both for the general case and for the geometric one; in the former case, the network topology is represented by a generic graph with arbitrary weights, whereas in the latter a Euclidean distance is considered. We then describe a new heuristic, Embedded Wireless Multicast Advantage. We show that it compares well with other proposals and we explain how it can be distributed. Categories and Subject Descriptors

We consider the problem of correlated data gathering by a network with a sink node and a tree communication structure, where the goal is to minimize the total transmission cost of transporting the information collected by the nodes, to the sink node. Two coding strategies are analyzed: a SlepianWolf model where optimal coding is complex and transmission optimization is simple, and a joint entropy coding model with explicit communication where coding is simple and transmission optimization is difficult. This problem requires a joint optimization of the rate allocation at the nodes and of the transmission structure. For the Slepian-Wolf setting, we derive a closed form solution and an efficient distributed approximation algorithm with a good performance. For the explicit communication case, we prove that building an optimal data gathering tree is NPcomplete and we propose various distributed approximation algorithms.

In a sensor network, the infrastructure (in terms of the sensor capabilities, number of sensors, and deployment strategy) plays a significant role in determining the performance of the network. In this paper, we study the effect of infrastructure decisions on the performance of a sensor network. We study the effect of the infrastructure for two types of network delivery models (phenomenon driven and continuous) and different network protocols (DSR, DSDV and AODV). We show the performance both in terms of network efficiency as well as meeting the application accuracy and latency demands. By exploring the criteria for effective infrastructure configurations, we open the door for network optimizations that control the effective topology to better achieve the application requirements.